Interactive Ads Development

1–2 seconds to stop the scroll on platforms like Instagram or TikTok. Users form an opinion about a visual in ~50 milliseconds. Want to instantly grab attention? Great visual composition isn’t just about aesthetics, it’s about direction. Content with compelling visuals gets 94% more views than text-only content. It leads the viewer’s eye, shapes how your message is understood, and makes your content impossible to ignore. 8 essential principles to level up your visual game: 1. Rule of Thirds Break your frame into a 3x3 grid. Positioning key elements along these lines or at their intersections creates a naturally balanced and pleasing layout. 2. Leading Lines Incorporate lines, whether architectural, natural, or implied, to pull the viewer’s gaze toward your focal point or guide them through the composition. 3. Balance Create stability by distributing elements thoughtfully. This can be perfectly symmetrical or more dynamic and asymmetrical, depending on the visual weight. 4. Focal Point Every design needs a clear star. This is the element that immediately captures attention and anchors the composition. Clear visual hierarchy can improve conversion rates by up to 30% by reducing cognitive load and guiding decisions. 5. Negative Space What you leave out matters. Space around elements enhances clarity, improves readability, and gives your design room to breathe. 6. Hierarchy & Scale Use size, placement, and proportion to signal importance. This helps viewers navigate your design in a clear, intentional flow. Applying hierarchy, contrast, and spacing can increase content comprehension by up to 70% 7. Contrast Play with differences, color, size, shape, or texture, to create emphasis and depth. Contrast is what makes elements pop. High-contrast CTAs (buttons, key elements) can increase CTR by 20–40% in digital campaigns. 8. Repetition Consistent use of shapes, colors, or patterns builds rhythm and cohesion, making your design feel unified and intentional. Consistent visual systems can increase brand recognition by up to 80% Final Thought Visual structure isn’t optional, it’s how we make sense of what we see. As creators, it’s our job to shape that experience. Master these principles, and your designs won’t just look good, they’ll communicate with clarity and impact. Explore references, study great work, and keep refining your eye. #beautybusiness #beautyvisuals #keyvisuals #communication

I've been thinking a lot about the kind of content brands put into the world. Some of it sparks conversation and strengthens brand connection. Some of it...just fills the feed. Most B2C brands are great at chasing engagement, but not always at building brand meaning. When I mapped it out, the content that matters most always ends up in the upper-right quadrant: High Engagement + High Cultural Relevance / Emotional Impact. 🟩 The Sweet Spot This is content people actually interact with and that strengthens brand connection: • User-Generated Storytelling (not just reviews, but authentic, emotional UGC) • Lifestyle & Aspirational Content (travel inspo, fashion, wellness — fits seamlessly into how people see themselves) • Viral TikTok/Reels Trends (when done authentically and in sync with culture) • Influencer Collaborations (especially when creators embody your brand values) • Community Challenges / Hashtag Activations (identity-driven and participatory) This is where loyalty gets built. Where campaigns outlive algorithms. Where engagement means something. ⸻ 🟧 What to Watch Out For (Low/Low) • Generic Product Ads (feature dumps without story) • Random Sales Promotions (uninspired discount graphics) • Forced Trend-Jacking (when brands hop on memes without fit) 👉 These pieces don’t move the needle on culture or engagement. ⸻ 🟪 The Trap (High Engagement / Low Relevance) • Giveaways / Sweepstakes (quick hits, low equity) • Funny Memes / Low-lift Humor (attention-grabbing but not tied to your brand) • Clickbait-y Hacks (drive views without deepening connection) • Flash Discounts (transactional, not relational) 👉 Yes, these light up the metrics — but they don’t build lasting brand affinity. ⸻ The takeaway? Don’t just chase clicks. Make more content for the upper right: where engagement fuels cultural relevance, and cultural relevance and emotional impact fuels long-term brand love. 𝙄𝙛 𝙮𝙤𝙪 𝙝𝙖𝙫𝙚𝙣’𝙩 𝙨𝙚𝙚𝙣 𝙢𝙮 𝘽2𝘽 𝙢𝙖𝙩𝙧𝙞𝙭, 𝙘𝙝𝙚𝙘𝙠 𝙞𝙩 𝙤𝙪𝙩 𝙝𝙚𝙧𝙚: https://lnkd.in/d7DXQDMB 𝙄’𝙡𝙡 𝙙𝙞𝙫𝙚 𝙙𝙚𝙚𝙥𝙚𝙧 𝙞𝙣𝙩𝙤 𝙘𝙤𝙣𝙩𝙚𝙣𝙩 𝙞𝙣 𝙪𝙥𝙘𝙤𝙢𝙞𝙣𝙜 𝙄𝙣𝙨𝙞𝙙𝙚 𝙎𝙤𝙘𝙞𝙖𝙡 𝙈𝙚𝙙𝙞𝙖 𝙇𝙚𝙖𝙙𝙚𝙧𝙨𝙝𝙞𝙥 𝙣𝙚𝙬𝙨𝙡𝙚𝙩𝙩𝙚𝙧𝙨. 𝙎𝙪𝙗𝙨𝙘𝙧𝙞𝙗𝙚 𝙝𝙚𝙧𝙚: https://lnkd.in/d28dna4K

When luxury visuals drive engagement without weakening desirability Luxury brands are under growing pressure to perform on social media, but performance in this space cannot be reduced to reach, frequency, or content volume alone. The real issue is more strategic: how to generate engagement in open digital environments without eroding the visual discipline that sustains desirability. Recent research on luxury-related Instagram content suggests that some visual characteristics matter more than others. Simplicity and self-similarity appear to support engagement consistently, while symmetry has a more variable effect depending on the category, and contrast on its own seems to matter far less. This should interest luxury executives far beyond the communications team. Visual consistency is not just a matter of aesthetics or brand taste. It is part of brand governance. If consumers process an image quickly and coherently, they are more likely to engage with it. That idea is aligned with the broader literature on processing fluency, which has long shown that stimuli that are easier to process tend to be judged more positively. In a luxury context, this means that coherent visual systems may strengthen both recognition and response, without forcing the brand into louder or more promotional codes. There is also a practical management lesson here. Luxury brands often speak about storytelling, but too many digital ecosystems are built as content pipelines rather than as controlled semiotic systems. The consequence is familiar: strong campaigns surrounded by weak day-to-day execution, inconsistent art direction across markets, and engagement tactics that boost visibility while blurring identity. Earlier research on luxury brands on Instagram also points to the importance of how visual elements are structured and presented, rather than simply whether the brand is active on the platform. In other words, digital success in luxury depends less on doing more and more on creating a visual language that remains recognizable, selective, and coherent over time. For business leaders, the implication is clear. Social media should not be managed only as a publishing function. It should be treated as a brand architecture issue with consequences for desirability, perceived value, and long-term equity. The brands that will win online are not necessarily those that produce the most content, but those that understand how visual fluency, consistency, and restraint can support both engagement and prestige. In luxury, digital performance is strongest when expression remains controlled. I help luxury brands and premium businesses sharpen their positioning, strengthen brand coherence across markets and channels, and grow without weakening desirability. #Luxury #LuxuryMarketing #BrandStrategy #DigitalStrategy #SocialMediaMarketing

Stop Making Videos No One Watches We all know video is king, but let's be real—most of our content gets scrolled past faster than free food disappears at networking events. The stats don't lie: Videos with visual hooks get 27% higher completion rates and drive 41% more engagement than standard content. That's not just marginal gains—that's the difference between wasted effort and actual results. Try these 5 visual hooks that might look silly but are proven attention-grabbers: ✅The Falling Hook – Real estate agents using falling house keys or price tags saw 34% higher engagement (Coldwell Banker saw inquiries jump after implementing this in listing videos) ✅The Tapping Hook – Financial advisors using finger taps to reveal investment returns captured 29% longer watch time (Fidelity's retirement calculators use this brilliantly) ✅The Color Change Tap – Healthcare providers switching colors when highlighting critical services increased click-through rates by 38% (Mayo Clinic's symptom videos are masterclasses in this) ✅The Appearing Text – Tech companies revealing key stats with pop-up text improved information retention by 43% (Microsoft's product launches leverage this perfectly) ✅The Magic Hook – E-commerce brands using disappearing/reappearing product features saw 52% higher conversion rates (Nike's product reveals are legendary here) Is it sometimes goofy? Yes. Does it work? Absolutely. What visual hook will you try in your next post? #ContentStrategy #VideoMarketing #VisualHooks #LinkedInTips

Programmatic buyers who recognize the flaws in user-based attribution will appreciate this approach. In platform we measure the impact of each channel/tactic on conversion pixel fires over time, which has proven more effective than user-based attribution in numerous cases. Instead of the traditional user-based attribution method (“I showed this user an ad, I cookie the user, and if within 30 days they fire the pixel, the last ad shown gets credit”), we measure how spend in each channel/tactic impacts pixel fires. Our methodology: “I’m allocating spend to this channel/tactic; over time, we observe its impact on total conversion pixel fires and adjust budgets based on each channel/tactic’s effectiveness in driving those fires.” This enables more accurate measurement of hard-to-track channels in-platform, such as CTV, or environments where user ID is blocked or absent (e.g., iOS). It also eliminates lower-funnel bias and budget waste on organic conversions, a frequent user-based attribution pitfall. Prospecting spend has a longer attribution window but still demonstrates impact. Over-prioritizing retargeting and lower-funnel tactics reduces overall impact by depleting budget from channels/tactics that feed the lower funnel. We can still measure and report user-based attribution to clients. However, we present this impact analysis and allocate budgets based on measured impact.

I've spent at least seven-figures in LinkedIn Ads across B2B SaaS companies, and I keep seeing the same painful mistake: Teams treat budget allocation like a finger-in-the-air exercise. Someone asks, "What should we spend on LinkedIn?" and the answer comes back as "Let's start with $3k and see what happens". No math. No model. No meaningful way to know if you're underspending or lighting money on fire. This approach guarantees one thing: You'll never know what's actually working. Here's what I use to set budgets that account for market reality: Budget = Audience x Penetration x Frequency) x (CPM/1000) Let me break each variable and why it matters: - Audience: This is your actual number of profiles that match your ICP criteria on LinkedIn. If you're selling to VP+ of Sales persona at SaaS companies with 50-200 employees in NAMER, you might have 8k accounts. Maybe less. But get the exact number from LinkedIn campaign manager. - Penetration: What % of the above audience do you realistically need to reach each month? Most folks assume 100%. In reality, 50-70% penetration is strong. Why? Things like frequency caps, user activity patterns, budget pacing, and auction competition all limit your ability to blanket-cover your audience. Plus, not everyone is doom-scrolling over here. - Frequency: This is where many folks get it wrong. They either hit someone once and wonder why nobody remembers them or blast them 30 times and burn out the relationship before it even starts. The ideal range I try to hit is 10-15 in the last 30 days. That's about 3-4 impressions per week. - CPM: This fluctuates based on targeting specificity, creative quality, bid strategy, and competitive density in your market. I typically start with $130 if I don't have any data and then rerun my projections after I have some data. Quick example: - Audience: 7900 targetable profiles - Penetration: 60% - Frequency: 10/month - CPM: $130 That gives us 7900 x 0.6 x 10 x ($130/1000) = $6,162/mo That's your baseline monthly budget to achieve meaningful reach and frequency. The good thing about this is it gives you defendable budget tied to actual market math. So when your leadership team asks why you need $6K/mo, your response is strong. And this framework scales. Running multiple segments? You can calculate budgets for each segment and roll it up to give you your overall budget. Another deeper insight many miss: You've indirectly also set leading indicators. Penetration & frequency drives brand lift and pipeline contribution far more than raw budget size. I've seen $21k/mo budgets perform worse than $10k/mo budgets because the larger one was spread too thin across poorly segmented audiences. You hit 40% of five different segments at 4 frequency each... nobody remembers you. The smaller budget hits 65% of the one well-defined segment at 10 frequency... that audience actually knows who you are. This why precision beats volume on LinkedIn.

90% of brands are wasting their Google ads budget. Here's why: Most brands are throwing money at Google with ZERO strategy behind their allocation. Meanwhile, the brands crushing it right now have a precise framework for budget distribution that's dramatically increasing their profit. After managing millions in Google ad spend, here's what each campaign type is ACTUALLY best at: 🔍 Search Ads: - Highest intent traffic (they're literally typing what they want) - Most control over targeting and messaging - Best for complex products that need explanation - BUT: Limited scale and higher CPCs 🛍️ Shopping Ads - Product-focused visual format - Strong for price-competitive products - Direct product comparisons - BUT: No ability to add compelling copy, challenging with high priced products 🚀 Performance Max - Access to all Google properties in one campaign - AI-driven audience finding - Great for scaling when other campaigns plateau - BUT: "Black box" with limited visibility, can cannibalize other campaigns Here's the budget allocation framework that's working in 2025: For Brands Under $20K/Month Ad Spend: - 60% Shopping (gather data and find winning products) - 30% Search (high-intent traffic) - 10% Performance Max (testing) (Best to lean into standard shopping at low spend rather than pMax.) For Brands $20K-$50K/Month: - 30% Shopping (push all products) - 30% Search (scale winning keywords using broad) - 40% Performance Max (scale winning products) For Brands $50K+/Month: 20% Shopping (more a 'catch all' defensive campaign) 20% Search (you'll probably have hit a ceiling on search by now) 60% Performance Max (expand & spend more on TOF placements) BUT – these allocations should shift based on 4 critical factors: 1️⃣ For complex products: Increase Search (+10-15%), Decrease PMax (-5-10%) and send traffic to an education landing page. 2️⃣ For highly visual products: Increase Shopping (+10%), Decrease Search (-15%) best for fashion products etc. 3️⃣ In competitive niches: Decrease Search (-10-20%), Increase PMax (+10-20%) for cheaper CPC's. 4️⃣ For new accounts: Avoid pMax (until you have 50 sales per month.) Implementation tip: Don't make drastic overnight changes – Google's algorithm needs time to adapt. Shift 10-15% of budget per week and monitor 7-day performance. Agree or disagree? 👇

Planning media budgets can be complicated; Last year’s run rates/ historic performance, market conditions, competitor impact, strategy changes, financial targets, etc all need to be integrated into your performance marketing planning.   One metric that is important in the decision making process is that of marginal ROAS.   In simple terms, Marginal ROAS shows the return from the next unit of spend. It tells you where the next £ invested will deliver the greatest return.   That makes marginal ROAS more powerful than blended or average ROAS, which can disguise underperforming spend hidden inside “good enough” averages. With marginal ROAS, you’re asking: if I put the next £ into this channel, how much incremental revenue will it actually generate?   How to get “the” number isn’t as easy as pulling Marginal ROAS directly out of a platform report. It requires modelling the relationship between spend and revenue per channel. Revenue attribution can be tricky to is trickier. Whether you’re using in platform conversions, (GA, Adobe, etc) or your own custom model (reported > restated numbers from SAP for example), you need a defensible way of assigning revenue back to that channel.   Once you’ve got that dataset, you model the spend<>revenue curve. This can be done at different levels of sophistication: anything from simple log trendlines in excel to advanced Bayesian regression. The slope of that curve at your current spend point is your Marginal ROAS.   It’s important to state that every channel follows diminishing returns and thus budget allocation, and understanding this at a detailed channel level is critical. The more you invest, the weaker the return from each additional unit of spend. That’s why Marginal ROAS is so powerful: it allows you to move money dynamically between channels, instead of sticking to static allocations.   When you work this way, your media planning becomes less about defending budgets and more about chasing efficiency > you will make smarter marketing mix decisions. If you’ve got an extra £50k to deploy for example, you know which channel should get it. If you need to cut, you know exactly where to pull from without losing incremental growth.   Over time, as your modelling matures, you can layer in more advanced measures; like moving from Marginal ROAS to Net Profit ROAS or even LTV-based views of incrementality. That’s where the real strategic allocation work begins; but Marginal ROAS is often the foundation you build from.   Finding the optimal media mix through trial and error can be slow + sub optimal if you do not “punch smart”. Advanced modelling and simulations can shortcut that process, showing you how different budget distributions will play out. These approaches give you a forward looking lens, instead of just reacting to performance curves after the fact. Note: image courtesy of segment stream

I scaled an app to $1.5M/month in ad spend. Here’s how I’d do it in 2026: 1. Under $50k in ad spend: Meta only Stick to Meta unless you’re a TikTok-first product. Meta still has the widest purchaser pool and the strongest potential performance. Focus on creative diversity here. Andromeda rewards accounts that test multiple formats, hooks, and angles. This is where you build your creative muscle before expanding. If you’re on Android, test Google UAC with 10–15% of budget. If you’re marketing a game, test an ad network like AppLovin early. They perform well for gaming. 2. $50k–$150k in ad spend: Introduce Google (iOS) / TikTok Start testing new channels like Google and TikTok. Allocate $5k–$10k as a test budget for each platform. For TikTok: creative matters more than targeting. Expect to test 20–30 creatives before finding what works. For Google iOS: focus on App Campaigns. Let the algorithm optimize while you feed it strong creative assets. Evaluate performance over 2–3 weeks minimum before judging scalability. Don’t spread budget thin. It’s better to test one channel properly than two channels poorly. 3. $150k–$250k in ad spend: Introduce ad networks Channels like AppLovin or Unity can be very effective at this stage. Be aware these channels have a learning phase, often 2–4 weeks before performance stabilizes. Start with broad targeting and let the algorithms find your users. Creative requirements are different here. Focus on playable ads, interactive end cards, and short-form video. Give them time and budget to learn before judging performance. 4. $250k+ in ad spend: Programmatic channels Programmatic channels can be massively scalable, but they come with a long learning curve. These may require $30k–$40k in ad spend (sometimes more) before you see consistent results, depending on your CPA and event volume. They’re best introduced once you have a solid foundation with other channels and a clear understanding of your LTV. Expect longer attribution windows and more complex optimization. We scaled programmatic to over $500k/month, but only after we had the infrastructure to support it. The channels may evolve. But the principle is evergreen. Progressive expansion unlocks scale gradually and profitably. Don’t try to be 40 before you’re 40.

Most companies are sitting on a goldmine of content they'll never use. It's a paradox. We're tasked with creating learning experiences, but we're already drowning in a sea of existing content: webinars, PDFs, videos, and knowledge bases. Your team isn't looking for more content. They're looking for the right content. The good news? If your organization has already invested in a Content Management System (CMS), Digital Asset Management (DAM), or a single-source publishing system, you are miles ahead of the competition. You've already done the hard work of creating structured repositories with rich metadata. This structure is rocket fuel for Generative AI, making it dramatically easier to transform those assets into personalized learning experiences. The old model of manually creating static, one-size-fits-all courses is broken. The future isn't about being a content creator. It's about being a content architect, and AI is the new toolkit. It’s a two-part system: 1. AI-Powered Curation This is about finding the right content at the right time. Instead of manually searching, AI can instantly: ▪️Discover relevant assets from across your entire organization. ▪️Organize them into logical paths. ▪️Deliver the precise answer a learner needs, exactly when they need it. 2. AI-Powered Adaptation This is about transforming that content to meet diverse needs. Once AI finds the right asset, it can instantly: ▪️Translate it into dozens of different languages for a global team. ▪️Convert its format—turning a dense document into a summary, an audio file for a commute, or a short instructional video. ▪️Personalize the information to an individual’s specific role, skill gaps, and career goals. Our role is shifting from building courses to designing intelligent systems. Systems that leverage existing assets to create truly personalized, on-demand learning experiences. How is your organization preparing to shift from static content libraries to dynamic, AI-powered learning environments?